cambridge-mlg/acp
Implementation for the paper "Approximating full conformal prediction at scale via influence functions"
This tool helps data scientists and machine learning engineers get reliable predictions from their models, especially with large datasets. It takes any differentiable machine learning model and a dataset, then outputs a 'prediction set' for each input that is guaranteed to contain the correct answer with a probability you define. This means you can be confident about the range of possible outcomes from your model.
No commits in the last 6 months.
Use this if you need to provide a set of predictions from your machine learning model that is guaranteed to include the true label with a high, specified probability, especially when working with extensive datasets.
Not ideal if you only need a single best prediction from your model and do not require quantifiable confidence guarantees or prediction sets.
Stars
11
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Apr 25, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cambridge-mlg/acp"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
zillow/quantile-forest
Quantile Regression Forests compatible with scikit-learn.
valeman/awesome-conformal-prediction
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers,...
yromano/cqr
Conformalized Quantile Regression
henrikbostrom/crepes
Python package for conformal prediction
xRiskLab/pearsonify
Lightweight Python package for generating classification intervals in binary classification...